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Estimating litchi flower number using a multicolumn convolutional neural network based on a density map

文献类型： 外文期刊

作者： Lin, Jiaquan 1 ; Li, Jun 1 ; Yang, Zhou 2 ; Lu, Huazhong 2 ; Ding, Yunhe 1 ; Cui, Huajun 1 ;

作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China

2.South China Agr Univ, Key Lab Key Technol Agr Machine & Equipment, Minist Educ, Guangzhou 510642, Peoples R China

3.Jiaying Univ, Guangdong Prov Key Lab Conservat & Precis Utiliza, Meizhou 514015, Peoples R China

4.Guangdong Acad Agr Sci, Guangzhou 510640, Peoples R China

关键词： Flowering intensity estimation; Multicolumn convolutional neural network; Density map; Flower number; Male litchi flower

期刊名称：PRECISION AGRICULTURE （ 影响因子：5.767； 五年影响因子：5.875 ）

ISSN： 1385-2256

年卷期： 2022 年 23 卷 4 期

页码：

收录情况： SCI

摘要： To ensure litchi fruit yield and quality, reasonable blooming period management such as flower thinning is required during the early flowering period. A combination of the number of litchi flowers and their density map can provide a reference for blooming period management decisions during the flowering period. Flowering intensity is currently largely estimated manually by humans observing the trees in the orchard. Although some automatic computer vision systems have been proposed for estimating flowering intensity, their overall performance is inadequate to meet current flower-thinning needs, and these systems are weak in some situations such as in varying environments and when the target object has a low-density distribution. With male litchi flowers as the research object, the goal of this study was to design a method for calculating the number of flowers. By using an image of the male litchi flower as input to a multicolumn convolutional neural network, a final density map and the number of flowers were generated. Experimental results using a self-constructed male litchi flower dataset demonstrated the feasibility of outputting a density map and flower count. The flower number was estimated from the model with a mean absolute error (MAE) that reached 16.29 and a mean square error (MSE) reaching 25.40 on the test set, which was better than counting by target detection. The proposed method can be used to perform time-saving analyses to help estimate yield and implement follow-up orchard management, and it demonstrates the potential of using density maps as outputs for estimating flowering intensity.

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